Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and\nreliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost\nby preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the\nother side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application\napproaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical\nnonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce\nredundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific\nfield of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant\napproaches through numerical experiments and simulations.
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